Modeling carbon dynamics from a heterogeneous watershed in the mid-Atlantic USA: A distributed-calibration and independent verification (DCIV) approach.

The terrestrial ecosystem plays a vital role in regulating regional and global carbon budgets. Ecosystem models are extensively employed to estimate carbon fluxes across different spatial scales. However, there remains a need to reduce the uncertainties associated with model parameterization and input data. To address these limitations, we assessed a distributed-calibration and independent-verification (DCIV) approach that uses (1) remotely sensed net primary production (NPP) and evapotranspiration (ET) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), (2) multi-site eddy covariance net ecosystem exchange (NEE) data; and (3) field sampling of soil organic carbon (SOC) and aboveground biomass (ABG) data to improve the overall predictability of carbon fluxes for the different land use and land cover (LULC) types at a watershed scale. The DCIV approach was applied to an advanced version of the Soil and Water Assessment Tool (SWAT)-Carbon (or SWAT-C), equipped with Century-based SOC algorithms to simulate carbon dynamics for watersheds with heterogeneous vegetation. The objective of the modeling effort was to assess carbon stocks and fluxes under different land management scenarios for a 3000-acre experimental farm and forest preserve in the northeastern United States. Our study showed that a large SOC stock of at least 100 tons ha-1 is stored under mixed forest, deciduous, shrubland, and floodplain (grass). Our study also showed that converting floodplain (grass) to deciduous forest has the potential to increase CO2 uptake (-NEE) by an order of three magnitude and ABG by 77 %, leading to an increased SOC stock of 23 % after twenty years. Similarly, we found that converting ungrazed grassland to grazed pasture leads to a non-statistically decreasing trend of SOC, especially in the 0-30 cm soil layer. Thus, the methodology used in this study can be applied to improve carbon dynamic prediction from a heterogeneous watershed at a regional scale.

Response of hydrology and nutrient losses to different extreme rainfall conditions in a coastal watershed influenced by orchards.

Global warming is altering the frequency of extreme rainfall events and introducing uncertainties for non-point source pollution (NPSP). This research centers on orchard-influenced planting areas (OIPA) in the Wulong River Watershed of Shandong Province, China, which are known for their heightened nitrogen (N) and phosphorus (P) pollution. Leveraging meteorological data from both historical (1989-2018) and projected future periods (2041-2100), this research identified five extreme rainfall indices (ERI): R10 (moderate rain), R20 (heavy rain), R50 (rainstorm), R95p (Daily rainfall between the 95th and 99th percentile of the rainfall), and R99p (>99th percentile). Utilizing an advanced watershed hydrological model, SWAT-CO2, this study carried out a comparison between ERI and average conditions and evaluated the effects of ERI on the hydrology and nutrient losses in this coastal watershed. The findings revealed that the growth multiples of precipitation in the OIPA for five ERI varied between 16 and 59 times for the historical period and 14 to 65 times for future climate scenarios compared to the average conditions. The most pronounced increases in surface runoff and total phosphorus (TP) loss were observed with R50, R95p, and R99p, showing growth multiples as high as 352 and 330 times, and total nitrogen (TN) growth multiples varied between 4.6 and 30.3 times. The contribution rates of R50 and R99p for surface runoff and TP loss in the OIPA during all periods exceeded 55%, however, TN exhibited the opposite trend, primarily due to the dominated NO3-N leaching in the sandy soil. This research revealed how the OIPA reacts to different ERI and pinpointed essential elements influencing water and nutrient losses.

Improving hydrological modeling to close the gap between elevated CO2 concentration and crop response: Implications for water resources.

Rising atmospheric carbon dioxide concentrations ([CO2]) affect crop growth and the associated hydrological cycle through physiological forcing, which is mainly regulated by reducing stomatal conductance (gs) and increasing leaf area index (LAI). However, reduced gs and increased LAI can affect crop water consumption, and the overall effects need to be quantified under elevated [CO2]. Here we develop a SWAT-gs-LAI model by incorporating a nonlinear gs-CO2 equation and a missing LAI-CO2 relationship to investigate the responses of water consumption of grain maize, maize yield, and losses of water and soil to elevated [CO2] in the Upper Mississippi River Basin (UMRB; 492,000 km2). Results exhibited enhanced maize yield with decreased water consumption for increases in [CO2] from 495 ppm to 825 ppm during the historical period (1985-2014). Elevated [CO2] promoted surface runoff but suppressed sediment loss as the predominant impact of LAI-CO2 leading to enhanced surface cover. A comprehensive analysis of future climate change showed increased maize water consumption in comparison to the historical period, driven by the more pronounced effects of overall climate change rather than solely elevated [CO2]. Generally, future climate change promoted maize yield in most regions of the UMRB for three Shared Socioeconomic Pathway (SSP) scenarios. Surface runoff was shown to increase generally in the future with sediment loss increasing by an average of 0.39, 0.42, and 0.66 ton ha-1 for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. This was due to negative climatic change effects largely surpassing the positive effect of elevated [CO2], particularly in zones near the middle and lower stream. Our results underscore the crucial role of employing a physically-based model to represent crop physiological processes under elevated [CO2] conditions, improving the reliability of predictions related to crop growth and the hydrological cycle.

The response of non-point source pollution to climate change in an orchard-dominant coastal watershed.

China is the largest global orchard distribution area, where high fertilization rates, complex terrain, and uncertainties associated with future climate change present challenges in managing non-point source pollution (NPSP) in orchard-dominant growing areas (ODGA). Given the complex processes of climate, hydrology, and soil nutrient loss, this study utilized an enhanced Soil and Water Assessment Tool model (SWAT-CO2) to investigate the impact of future climate on NPSP in ODGA in a coastal basin of North China. Our investigation focused on climate-induced variations in hydrology, nitrogen (N), and phosphorus (P) losses in soil, considering three Coupled Model Intercomparison Project phase 6 (CMIP6) climate scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5. Research results indicated that continuous changes in CO2 levels significantly influenced evapotranspiration (ET) and water yield in ODGA. Influenced by sandy soils, nitrate leaching through percolation was the principal pathway for N loss in the ODGA. Surface runoff was identified as the primary pathway for P loss. Compared to the reference period (1971-2000), under three future climate scenarios, the increase in precipitation of ODGA ranged from 15% to 28%, while the growth rates of P loss and surface runoff were the most significant, both exceeding 120%. Orchards in the northwest basin proved susceptible to nitrate leaching, while others were more sensitive to N and P losses via surface runoff. Implementing targeted strategies, such as augmenting organic fertilizer usage and constructing terraced fields, based on ODGA's response characteristics to future climate, could effectively improve the basin's environment.

Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data.

Remotely sensed products are often used in watershed modeling as additional constraints to improve model predictions and reduce model uncertainty. Remotely sensed products also enabled the spatial evaluation of model simulations due to their spatial and temporal coverage. However, their usability is not extensively explored in various regions. This study evaluates the effectiveness of incorporating remotely sensed evapotranspiration (RS-ET) and leaf area index (RS-LAI) products to enhance watershed modeling predictions. The objectives include reducing parameter uncertainty at the watershed scale and refining the model's capability to predict the spatial distribution of ET and LAI at sub-watershed scale. Using the Soil and Water Assessment Tool (SWAT) model, a systematic calibration procedure was applied. Initially, solely streamflow data was employed as a constraint, gradually incorporating RS-ET and RS-LAI thereafter. The results showed that while 14 parameter sets exhibit satisfactory performance for streamflow and RS-ET, this number diminishes to six with the inclusion of RS-LAI as an additional constraint. Furthermore, among these six sets, only three effectively captured the spatial patterns of ET and LAI at the sub-watershed level. Our findings showed that leveraging multiple remotely sensed products has the potential to diminish parameter uncertainty and increase the credibility of intra-watershed process simulations. These results contributed to broadening the applicability of remotely sensed products in watershed modeling, enhancing their usefulness in this field.

Assessing watershed-scale impacts of best management practices and elevated atmospheric carbon dioxide concentrations on water yield.

Changes in water yield are influenced by many intersecting biophysical elements, including climate, on-land best management practices, and landcover. Large-scale reductions in water yield may present a significant threat to water supplies globally. Many of these intersecting factors are intercorrelated and confounded, making it challenging to separate the factors' individual contributions to shaping local streamflow dynamics. Comprehensive hydrological models constructed based on a well-established understanding of biophysical processes are often employed to address these matters. However, these models rarely incorporate all relevant factors influencing local hydrological processes, due to the reliance of these models on the latest, albeit limited, state-of-the-art research. For instance, complexities inherent in watershed hydrology, which involve multilayered interactions among potentially many biophysical factors, leave the direct analysis of subtle impacts on water yields measured in-situ largely intractable. Therefore, we propose an innovative approach to assess impacts of elevated atmospheric CO2 concentrations and flow diversion terraces (FDTs) on stream discharge rates at the watershed scale. Initially, we use a comprehensive hydrological model to account for the impacts of major climatic and landuse/landcover factors on changes in field-acquired measurements of water yield. Next, we employ conventional and advanced statistical methods to decompose the residuals of model predictions to facilitate the identification of subtle influences promoted by increases in atmospheric CO2 concentrations and the application of FDTs in an agriculture-dominated watershed. Through this innovative approach, we find that FDTs contributed to a watershed-wide, net water-yield reduction of 188.0 mm (or 28.9 %) from 1992 to 2014. Ongoing increases in ambient CO2 concentrations, which are responsible for an overall reduction in a watershed-level assessment of stomatal conductance, have led to a minor increase in stream discharge rates during the same 23-year period, i.e., 0.45 mm of water yield per year, or 1.6 % overall. Streamflow reductions explicitly caused by regional warming in the area alone, on account of increased evapotranspiration, may be overestimated due to the opposing, synergistic effects on water yield associated with CO2-enrichment of the lower atmosphere and the annual application of FDTs.

Modeling lateral carbon fluxes for agroecosystems in the Mid-Atlantic region: Control factors and importance for carbon budget.

Estimating lateral carbon fluxes in agroecosystems presents challenges due to intricate anthropogenic and biophysical interactions. We used a modeling technique to enhance our comprehension of the determinants influencing lateral carbon fluxes and their significance in agroecosystem carbon budgets. The SWAT-C model was refined by incorporating a dynamic dissolved inorganic carbon (DIC) module, enhancing our ability to accurately quantify total lateral carbon fluxes. This improved model was calibrated using observed data on riverine particulate organic carbon (POC) and dissolved organic carbon (DOC) fluxes, as well as net ecosystem exchange (NEE) data monitored by a flux tower situated in a representative agricultural watershed, the Tuckahoe Watershed (TW) of the Chesapeake Bay's coastal plain. We assessed the losses of POC, DOC, and DIC across five primary rotation types: C (continuous carbon), CS (corn-soybean), CSS (corn-soybean-soybean), CWS (corn-wheat-soybean), and CWSCS (corn-wheat-soybean-corn-soybean). Our study revealed notable variations in the average annual fluxes of POC (ranging between 152 and 198 kg ha-1), DOC (74-85 kg ha-1), and DIC (93-156 kg ha-1) across the five rotation types. The primary influencing factor for annual POC fluxes was identified as sediment yield. While both annual DOC and DIC fluxes displayed a marked correlation with surface runoff across all crop rotation schemes, soil respiration also significantly influenced annual DIC fluxes. Total lateral carbon fluxes (POC + DOC+DIC) constituted roughly 11 % of both net ecosystem production (NEP) and NEE, yet they represented a striking 95 % of net biome production (NBP) in the TW's agroecosystem. Grain yield carbon accounted for 80 % of both NEP and NEE and was nearly seven times that of NBP. Our findings suggest that introducing soybeans into cornfields tends to reduce NEP, NEE, and also NBP. Conversely, integrating winter wheat into the corn-soybean rotation significantly boosted NEP, NEE, and NBP values, with NBP even surpassing the levels in continuous corn cultivation.

Simulated nitrous oxide emissions from multiple agroecosystems in the U.S. Corn Belt using the modified SWAT-C model.

Agriculture is a major source of nitrous oxide (N2O) emissions into the atmosphere. However, assessing the impacts of agricultural conservation practices, land use change, and climate adaptation measures on N2O emissions at a large scale is a challenge for process-based model applications. Here, we integrated six N2O emission algorithms for the nitrification processes and seven N2O emission algorithms for the denitrification process into the Soil and Water Assessment Tool-Carbon (SWAT-C). We evaluated the different combinations of methods in simulating N2O emissions under corn (Zea mays L.) production systems with various conservation practices, including fertilization, tillage, and crop rotation (represented by 14 experimental treatments and 83 treatment-years) at five experimental sites across the U.S. Midwest. The SWAT-C model exhibited wide variability in simulating daily average N2O emissions across treatment-years with different method configurations, as indicated by the ranges of R2, NSE, and BIAS (0.04-0.68, -1.78-0.60, and -0.94-0.001, respectively). Our results indicate that the denitrification process has a stronger impact on N2O emissions than the nitrification process. The best performing N2O emission algorithms are those rooted in the CENTURY model, which considers soil pH and respiration effects that were overlooked by other algorithms. The optimal N2O emission algorithm explained about 63% of the variability of annual average N2O emissions, with NSE and BIAS of 0.60 and -0.033, respectively. The model can reasonably represent the impacts of agricultural conservation practices on N2O emissions. We anticipate that the improved SWAT-C model, with its flexible configurations and robust modeling and assessment capabilities, will provide a valuable tool for studying and managing N2O emissions from agroecosystems.

Assessment of the sustainability of groundwater utilization and crop production under optimized irrigation strategies in the North China Plain under future climate change.

Over-exploitation of groundwater due to intensive irrigation and anticipated climate change pose severe threats to the water and food security worldwide, particularly in the North China Plain (NCP). Limited irrigation has been recognized as an effective way to improve crop water productivity and slow the rapid decline of groundwater levels. Whether optimized limited irrigation strategies could achieve a balance between groundwater pumping and grain production in the NCP under future climate change deserves further study. In this study, an improved Soil and Water Assessment Tool (SWAT) model was used to simulate climate change impacts on shallow groundwater levels and crop production under limited irrigation strategies to suggest optimal irrigation management practices under future climate conditions in the NCP. The simulations of eleven limited irrigation strategies for winter wheat with targeted irrigations at different growth stages and with irrigated or rainfed summer maize were compared with future business-as-usual management. Climate change impacts showed that mean wheat (maize) yield under adequate irrigation was expected to increase by 13.2% (4.9%) during the middle time period (2041-2070) and by 11.2% (4.6%) during the late time period (2071-2100) under three SSPs compared to the historical period (1971-2000). Mean decline rate of shallow groundwater level slowed by approximately 1 m a-1 during the entire future period (2041-2100) under three SSPs with a greater reduction for SSP5-8.5. The average contribution rate of future climate toward the balance of shallow groundwater pumping and replenishment was 62.9%. Based on the simulated crop yields and decline rate of shallow groundwater level under the future climate, the most appropriate limited irrigation was achieved by applying irrigation during the jointing stage of wheat with rainfed maize, which could achieve the groundwater recovery and sustainable food production.

An improved global vegetation health index dataset in detecting vegetation drought.

Due to global warming, drought events have become more frequent, which resulted in aggravated crop failures, food shortage, larger and more energetic wildfires, and have seriously affected socio-economic development and agricultural production. In this study, a global long-term (1981-2021), high-resolution (4 km) improved vegetation health index (VHI) dataset integrating climate, vegetation and soil moisture was developed. Based on drought records from the Emergency Event Database, we compared the detection efficiency of the VHI before and after its improvement in the occurrence and scope of observed drought events. The global drought detection efficiency of the improved high-resolution VHI dataset reached values as high as 85%, which is 14% higher than the original VHI dataset. The improved VHI dataset was also more sensitive to mild droughts and more accurate regarding the extent of droughts. This improved dataset can play an important role in long-term drought monitoring but also has the potential to assess the impact of drought on the agricultural, forestry, ecological and environmental sectors.

Investigating hydrological transport pathways of dissolved organic carbon in cold region watershed based on a watershed biogeochemical model.

Dissolved organic carbon (DOC) is a significant component of regional and global carbon cycles and an important surface water quality indicator. DOC affects the processes of solubility, bioavailability and transport for a number of contaminants, such as heavy metals. Therefore, it is crucial to understand DOC fate and transport in the watershed and the transport pathways of DOC load. We modified a previously developed watershed-scale organic carbon model by incorporating the DOC load from glacier melt runoff and used the modified model to simulate periodic daily DOC load in the upper Athabasca River Basin (ARB) in the cold region of western Canada. The calibrated model achieved an overall acceptable performance for simulating daily DOC load with model uncertainties mainly from the underestimation of peak loads. Parameter sensitivity analysis indicates that the fate and transport of DOC load in upper ARB are mainly controlled by DOC production in the soil layers, DOC transport at the soil surface, and reactions in the stream system. The modeling results indicated that the DOC load is mainly from the terrestrial sources and the stream system was a negligible sink in the upper ARB. It also indicated that rainfall-induced surface runoff was the major transport pathway of DOC load in the upper ARB. However, the DOC loads transported by glacier melt runoff were negligible and only accounted for 0.02% of the total DOC loads. In addition, snowmelt-induced surface runoff and lateral flow contributed 18.7% of total DOC load, which is comparable to the contribution from the groundwater flow. Our study investigated the DOC dynamics and sources in the cold region watershed in western Canada and quantified the contribution of different hydrological pathways to DOC load, which could provide a useful reference and insight for understanding watershed-scale carbon cycle processes.

Assessing impacts of global climate change on water and food security in the black soil region of Northeast China using an improved SWAT-CO2 model.

Future climate change may have substantial impacts on both water resources and food security in China's black soil region. The Liao River Basin (LRB; 220,000 km2) is representative of the main black soil area, making it ideal for studying climate change effects on black soil. In this study, the Soil and Water Assessment Tool (SWAT) model was first initialized for the LRB. Actual evapotranspiration (ETa) values calculated using the Surface Energy Balance System (SEBS) model and city-level corn (Zea mays L.) yields were then used to calibrate the SWAT model. Finally, the SWAT model was modified to accept dynamic CO2 input and output crop transpiration, soil evaporation, and canopy interception separately to explore the impacts of future climate change on ET related variables and crop water productivity (CWP) in the LRB. Simulation scenario design included 22 General Circulation Models (GCMs) and 4 Shared Socioeconomic Pathways (SSPs) scenarios from the latest Coupled Model Intercomparison Project 6 (CMIP6) for two 30-year periods of 2041-2070 and 2071-2100. The predicted results showed a significant (P < 0.05) increase in air temperatures and precipitation in the LRB. In contrast, solar radiation decreased significantly and was most reduced for the SSP3-7.0 scenario. Reference evapotranspiration (ETo), ETa, and soil evaporation significantly increased in future scenarios, while canopy interception and crop transpiration showed significant reductions, particularly under the 2071-2100 SSP5-8.5 scenario. Overall, corn yield elevated considerably (P < 0.05) with the largest increase for the SSP5-8.5 scenario during 2071-2100. However, the SSP3-7.0 scenario indicated a significant decline in yield. Future changes in CWP were similar to those for corn yield, with significant increases in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. These findings suggested future climate change may have a positive impact on corn production in the black soil region of the LRB.

Irrigation plays significantly different roles in influencing hydrological processes in two breadbasket regions.

Agriculture is a major water user, especially in dry and drought-prone areas that rely on irrigation to support agricultural production. In recent years, the over-extraction of groundwater, exacerbated by climate change, population growth, and intensive agricultural irrigation, has led to a drop in water levels and influenced the hydrological cycle. Understanding changes in hydrological processes is essential for pursuing water sustainability. This study aims to estimate the amount and impact of irrigation on hydrological processes in two breadbasket regions, Jing-Jin-Ji (JJJ), China, and northern Texas (NTX), US. We used the Soil and Water Assessment Tool (SWAT) to explore spatiotemporal variations of irrigation from 2008 to 2013 and compared changes in hydrological processes caused by irrigation. The results indicated that deficit irrigation is more common in JJJ than in NTX and can reduce approximately 50 % of irrigation water use in areas with intensively irrigated cropland. The applied irrigation varies less over time in NTX but fluctuates in JJJ. Compared with NTX, the higher irrigation intensity in JJJ results in a more significant change in downstream peak streamflow of around 6 m3/s. Moreover, the difference in crop growing seasons can lead to different impacts of irrigation on hydrological processes. For example, the percentage change of surface runoff under real-world relative to the no-irrigation scenario was the greatest, around 40 %, in JJJ and NTX. However, the peak change occurred at different times, with the nearing maturity of winter wheat in May in JJJ and corn in August in NTX. The great potential to reduce groundwater extraction by adopting water conservation irrigation techniques calls for policies and regulations to help farmers shift towards more sustainable water management practices.

An improved daily standardized precipitation index dataset for mainland China from 1961 to 2018.

The standardized precipitation index (SPI), one of the most commonly used drought indicators, is widely used in the research areas of drought analysis and drought prediction in different fields such as meteorology, agriculture, and hydrology. However, its main disadvantage is the relatively coarse time resolution of one month. To improve the time resolution of SPI to identify flash droughts, we have refined the traditional SPI calculation method and developed a new multi-scale daily SPI dataset based on data from 484 meteorological stations in mainland China from 1961 to 2018. SPI data from three different sites (located in Henan, Yunnan, and Fujian Provinces) at the three-month timescale were analyzed by comparing with historically recorded drought events. We found that the new multi-scale daily SPI can effectively capture drought events in different periods and locations and identify the specific start and end times of drought events. In short, our SPI dataset appears reasonable and capable of facilitating drought research in different fields.

Pronounced Increases in Future Soil Erosion and Sediment Deposition as Influenced by Freeze-Thaw Cycles in the Upper Mississippi River Basin.

Soil erosion and sediment deposition are relevant to multiple important ecosystem services essential for natural and human systems. The present study aims to project future soil erosion and sediment deposition in the Upper Mississippi River Basin (UMRB) using climate projections by five Global Circulation Models (GCMs) under the Representative Concentrations Pathway (RCP) 8.5 scenario. To understand the importance of freeze-thaw cycles (FTCs) for soil erosion and sediment deposition estimation with climate change, this study compared two Soil and Water Assessment Tool (SWAT) models with different representations of the FTCs, with the standard SWAT using a simple regression method and SWAT-FT employing a physically based method. Modeling results show that future climate change can pronouncedly intensify soil erosion and increase sediment deposition, and the impacts are sensitive to how FTCs are represented in the model. The standard SWAT projected an increase in soil erosion by nearly 40% by the end of the 21st century, which is much lower than the projected over 65% increase in soil erosion by SWAT-FT. For sediment deposition, the projected percent changes by the standard SWAT and SWAT-FT also deviate from each other (i.e., about 70% by the standard SWAT vs about 120% by SWAT-FT). Overall, these results demonstrate the important roles of FTCs in projecting future soil erosion and sediment deposition and underline the need to consider the effects of conservation practices on FTCs to realistically assess the effectiveness of those measures.

An x-ray crosstalk correction method using FCNN for a novel energy resolving scheme in spectral CT.

Spectral computed tomography has great potential for multi-energy imaging and anti-artifacts. The complete absorption-based energy resolving scheme of x-rays has been used for the integrity of detected information. However, this scheme is limited by the fact that the detector pixel thickness is high and fixed. Here, an energy resolving scheme is proposed using the crosstalk correction method for the incomplete absorption detection of x-rays. A fully connected neural network (FCNN)-based method was used to correct the difference caused by internal x-ray crosstalk of the edge-on detector. The energy and spatial features of the data which is collected in layers were combined to establish the mapping between the ideal data and the data with crosstalk at the pre-processing stage. Thereafter, to reconstruct the stable and highly accurate energy-resolving equations, the layers with low relative energy difference were selected and grouped together to reduce the accumulation difference. The experiment results demonstrate the feasibility of this energy resolving scheme. The differences caused by crosstalk can be suppressed through the proposed FCNN-based method. The resolving accuracy can be further improved by grouping more layers at forward positions in the pixel. Moreover, this improvement can be observed in the reconstructed images with reduced artifacts and improved quality.

Nitrate loading projection is sensitive to freeze-thaw cycle representation.

Climate change can have substantial impacts on nitrogen runoff, which is a major cause of eutrophication, harmful algal blooms, and hypoxia in freshwaters and coastal regions. We examined responses of nitrate loading to climate change in the Upper Mississippi River Basin (UMRB) with an enhanced Soil and Water Assessment Tool with physically based Freeze-Thaw cycle representation (SWAT-FT), as compared with the original SWAT model that employs an empirical equation. Driven by future climate projections from five General Circulation Models (GCMs) from 1960 to 2099 under the Representative Concentrations Pathways (RCP) 8.5 scenario, we analyzed changes in riverine nitrate loadings, as well as terrestrial surface and subsurface contributions of the UMRB in the 21st century relative to the baseline period of 1960-1999. By the end of the 21st century, the original SWAT model predicted about a 50% increase in riverine nitrate loadings which is nearly twice as much as that estimated by SWAT-FT (ca. 25%). Such a large difference in projected nitrate changes can potentially mislead mitigation strategies that aim to reduce nitrogen runoff from the UMRB. Further analysis shows that the difference between the original SWAT model and SWAT-FT led to substantial discrepancies in the spatial distribution of surface and subsurface nitrate loadings in the UMRB. In general, SWAT-FT predicted more nitrate leaching for northwestern parts of the UMRB which are more sensitive to freeze-thaw cycle, mainly because SWAT-FT simulated less frequent frozen soils. This study highlights the importance of using physically based freeze-thaw cycle representation in water quality modeling. Design of future nitrogen runoff reduction strategies should include careful assessment of effects that land management has on the freeze-thaw cycles to provide reliable projection of water quality under climate change.

Modeling sediment diagenesis processes on riverbed to better quantify aquatic carbon fluxes and stocks in a small watershed of the Mid-Atlantic region.

BACKGROUND: Despite the widely recognized importance of aquatic processes for bridging gaps in the global carbon cycle, there is still a lack of understanding of the role of riverbed processes for carbon flows and stocks in aquatic environments. Here, we added a sediment diagenesis and sediment carbon (C) resuspension module into the SWAT-C model and tested it for simulating both particulate organic C (POC) and dissolved organic C (DOC) fluxes using 4 years of monthly observations (2014-2017) in the Tuckahoe watershed (TW) in the U.S. Mid-Atlantic region. RESULTS: Sensitivity analyses show that parameters that regulate POC deposition in river networks are more sensitive than those that determine C resuspension from sediments. Further analyses indicate that allochthonous contributions to POC and DOC are about 36.6 and 46 kgC ha-1 year-1, respectively, while autochthonous contributions are less than 0.72 kgC ha-1 year-1 for both POC and DOC (less than 2% of allochthonous sources). The net deposition of POC on the riverbed (i.e., 11.4 kgC ha-1 year-1) retained ca. 31% of terrestrial inputs of POC. In addition, average annual buried C was 0.34 kgC ha-1 year-1, accounting for only 1% of terrestrial POC inputs or 3% of net POC deposition. The results indicate that about 79% of deposited organic C was converted to inorganic C (CH4 and CO2) in the sediment and eventually released into the overlying water column. CONCLUSION: This study serves as an exploratory study on estimation of C fluxes from terrestrial to aquatic environments at the watershed scale. We demonstrated capabilities of the SWAT-C model to simulate C cycling from uplands to riverine ecosystems and estimated C sinks and sources in aquatic environments. Overall, the results highlight the importance of including carbon cycle dynamics within the riverbed in order to accurately estimate aquatic carbon fluxes and stocks. The new capabilities of SWAT-C are expected to serve as a useful tool to account for those processes in watershed C balance assessment.

Characterizing the impacts of land use on nitrate load and water yield in an agricultural watershed in Atlantic Canada.

Excessive nitrate loading from agricultural non-point source is threatening the health of receiving water bodies at the global scale. Quantifying the drivers/sources of water and nitrate flux in watersheds and relating them to spatial and temporal land uses is essential for developing effective mitigation strategies. This study investigated the impact of land use on water yield and nitrate loading to surface water in a typical agricultural watershed in Prince Edward Island (PEI), Canada. We used historical streamflow and water quality records to calibrate the comprehensive hydrological model Soil and Water Assessment Tool (SWAT), which was setup with detailed annual land use records. The SWAT model performed well in predicting both daily streamflow and nitrate load. Land use demonstrated little impact on water yield but affected nitrate load significantly. Annual nitrate load ranged from 5.6 to 44.4 kg N ha-1 yr-1 for forest and soybean, respectively. Potato rotated land contributed 84.5% of annual nitrate load to the watershed. Source of water yield demonstrated high variability between the growing season and non-growing season. About 90% of water yield was contributed by groundwater during growing season, while runoff contributed over 60% of water yield during the non-growing season. Groundwater was the dominant source of nitrate loading for both seasons. The watershed estuary faced the highest threats from subbasins in the south western area due to the high nitrate load and proximity to the watershed outlet. Results by the machine learning algorithm random Forest analysis indicated that the climatic variables of temperature and precipitation were the top two factors affecting water yield, with a combined relative importance of 61%. Land use was the dominant factor affecting nitrate load, the relative importance of land use alone was ~50%. The results of this study provided critical insights for watershed management in Atlantic Canada.

Ni(HCO3)2 nanosheet/nickel tetraphosphate (Ni(P4O11)) nanowire composite as a high-performance electrode material for asymmetric supercapacitors.

Nickel compounds, especially Ni(HCO3)2 (here denoted as NiC), have been widely combined with other materials to obtain composites with a more favorable structure that exhibit excellent electrochemical performance as supercapacitors. Unfortunately, the complicated processes for preparing such composites directly restrict their further application. Herein, we prepared a NiC/nickel tetraphosphate (Ni(P4O11)) nanocomposite (NiC/NiP) by introducing [Formula: see text] ions into the NiC reaction system; this composite can be applied in high-performance supercapacitors. The micromorphology of NiC/NiP material displayed an appropriate combination of NiP nanowires and thin NiC nanosheets, which provide sufficient active sites, short ion diffusion paths and fast ion diffusion speeds. NiC/NiP material exhibited an excellent rate performance of 70.2% retained capacity, although the current was increased by 15 times (1196 F g-1 at 2.0 A g-1 and 840 F g-1 at 30 A g-1). The energy density of a NiC/NiP//active carbon (AC) asymmetric supercapacitor fabricated in 6 M KOH was as much as 39.02 W h kg-1 and 26.67 W h kg-1 under corresponding power densities of 160 W kg-1 and 8000 W kg-1, respectively. The asymmetric supercapacitor delivered a stable cyclic performance of 78% capacitive retention after 5000 continuous charge/discharge cycles. More importantly, a 2.5 V light-emitting diode was lit successfully by two NiC/NiP//AC asymmetric supercapacitors in series. These results confirm that NiC/NiP nanocomposite has great potential in practical applications of electrochemical energy storage devices.